Table 5 Comparison of classification accuracy of different algorithms based on RF.
From: Attribute reduction based on classes in incomplete ordered decision systems
Datasets | HAR | Algorim2 | Algorim4 | MIFS | HANDR |
|---|---|---|---|---|---|
Iris | 0.9667 ± 0.0211 | 0.9333 ± 0.0632 | 0.9333 ± 0.0843 | 0.9673 ± 0.0209 | 0.9533 ± 0.0267 |
DARWIN | 0.6914 ± 0.0826 | 0.6848 ± 0.0216 | 0.6883 ± 0.0246 | 0.7005 ± 0.0126 | 0.7356 ± 0.0690 |
CB | 0.6528 ± 0.1462 | 0.6045 ± 0.0319 | 0.5952 ± 0.0433 | 0.6643 ± 0.1502 | 0.7160 ± 0.1241 |
Iono | 0.8348 ± 0.0365 | 0.7833 ± 0.0442 | 0.8202 ± 0.0483 | 0.8719 ± 0.0253 | 0.9033 ± 0.0562 |
BCWD | 0.8735 ± 0.0291 | 0.9086 ± 0.0274 | 0.9543 ± 0.0187 | 0.9192 ± 0.0245 | 0.9403 ± 0.0262 |
Statlog | 0.6734 ± 0.0342 | 0.5976 ± 0.0351 | 0.6249 ± 0.0321 | 0.6854 ± 0.0232 | 0.6154 ± 0.0506 |
Car | 0.7066 ± 0.0134 | 0.6776 ± 0.0132 | 0.6898 ± 0.0187 | 0.7066 ± 0.0134 | 0.7472 ± 0.0587 |
Card | 0.8749 ± 0.0209 | 0.9230 ± 0.0183 | 0.8622 ± 0.0359 | 0.8744 ± 0.0193 | 0.8749 ± 0.0209 |
AIDS | 0.8065 ± 0.0106 | 0.7915 ± 0.0296 | 0.8046 ± 0.0148 | 0.8154 ± 0.0104 | 0.8233 ± 0.0111 |
Chess | 0.7340±0.0321 | 0.7143 ± 0.0429 | 0.8184 ± 0.1404 | 0.7356 ± 0.0127 | 0.7340 ± 0.0321 |
ORHD | 0.7206 ± 0.0071 | 0.5632 ± 0.0098 | 0.6457 ± 0.0255 | 0.6571 ± 0.0094 | 0.7925 ± 0.0176 |
Mush | 0.8936 ± 0.1128 | 0.8931 ± 0.1685 | 0.8842 ± 0.1977 | 0.9162 ± 0.0639 | 0.9314 ± 0.0813 |
Average | 0.7857 ± 0.0456 | 0.7562 ± 0.0421 | 0.7768 ± 0.0570 | 0.7928 ± 0.0322 | 0.8139 ± 0.0479 |
